Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Automatic Requirements Engineering: Activities, Methods, Tools, and Domains – A Systematic Literature Review
Get PDFRequirements engineering (RE) is an initial activity in the software engineering process that involves many users. The involvement of various users in the RE process raises ambiguity and vagueness in requirements modeling. In addition, traditional RE is a time-consuming activity. Therefore various studies have been conducted to support process automation on RE. This paper conducts a systematic literature review (SLR) to obtain information about RE automation related to RE activities, methods/models, tools, and domains. SLR is done through 5 main stages: definition of research questions, conducting the search, screening for relevant papers, data extraction, mapping, and analysis. The data extraction and mapping are carried out on 155 relevant publications from 2016 to 2022. Based on the results from SLR, around 53% of the research focuses on RE automation in analysis and specifications, 40% focuses on elicitation, validation, and requirements management, and 7% focuses on requirements quality. NLP is the most used method in elicitation and specification, while for analysis, machine learning, NLP, and goal-oriented models are mostly used in automatic RE. Furthermore, many papers use specific models and methods for validation and requirements management. From the domain analysis results, it is obtained that more than half of the papers contribute directly to the RE domain, and some contribute to the development of RE automation in the software application domain.
Requirements engineering (RE) is an initial activity in the software engineering process that involves many users. The involvement of various users in the RE process raises ambiguity and vagueness in requirements modeling. In addition, traditional RE is a time-consuming activity. Therefore various studies have been conducted to support process automation on RE. This paper conducts a systematic literature review (SLR) to obtain information about RE automation related to RE activities, methods/models, tools, and domains. SLR is done through 5 main stages: definition of research questions, conducting the search, screening for relevant papers, data extraction, mapping, and analysis. The data extraction and mapping are carried out on 155 relevant publications from 2016 to 2022. Based on the results from SLR, around 53% of the research focuses on RE automation in analysis and specifications, 40% focuses on elicitation, validation, and requirements management, and 7% focuses on requirements quality. NLP is the most used method in elicitation and specification, while for analysis, machine learning, NLP, and goal-oriented models are mostly used in automatic RE. Furthermore, many papers use specific models and methods for validation and requirements management. From the domain analysis results, it is obtained that more than half of the papers contribute directly to the RE domain, and some contribute to the development of RE automation in the software application domain
Identification of Color and Texture of Ripe Passion Fruit with Perceptron Neural Network Method
No full textResearch using artificial neural network methods has been developed as a tool that can help human tasks, one of which is for passion fruit UMKM entrepreneurs. The problem so far that has been faced by UMKM entrepreneurs of passion fruit is that it is difficult to identify ripe passion fruit with sweet and sour taste, because there are 6 colors of passion fruit and the color of passion fruit skin is visually slightly different, as well as the texture of maturity. The main purpose of this study was to identify the color structure and texture of the ripeness of passion fruit, in order to recognize the color and texture of the ripeness of passion fruit which is good for processing into syrup, jam, jelly, juice, passion fruit juice powder by entrepreneurs of UMKM of passion fruit. This study empirically tested the color and texture of the ripeness of 10 passion fruit using the perceptron artificial neural network learning method. The data is obtained from an image that will be entered into the program. The results of the identification process using the perceptron artificial neural network from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.8 and 500 epoch iterations and producing an accuracy of 80%.
Research using artificial neural network methods has been developed as a tool that can help human tasks, one of which is for passion fruit UMKM entrepreneurs. The problem so far that has been faced by UMKM entrepreneurs of passion fruit is that it is difficult to identify ripe passion fruit with sweet and sour taste, because there are 6 colors of passion fruit and the color of passion fruit skin is visually slightly different, as well as the texture of maturity. The main purpose of this study was to identify the color structure and texture of the ripeness of passion fruit, in order to recognize the color and texture of the ripeness of passion fruit which is good for processing into syrup, jam, jelly, juice, passion fruit juice powder by entrepreneurs of UMKM of passion fruit. This study empirically tested the color and texture of the ripeness of 10 passion fruit using the perceptron artificial neural network learning method. The data is obtained from an image that will be entered into the program. The results of the identification process using the perceptron artificial neural network from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.8 and 500 epoch iterations and producing an accuracy of 80%
Capturing Students’ Dynamic Learning Pattern Based on Activity Logs Using Hierarchical Clustering
No full textStudents can have various characteristics and learning patterns. By understanding the characteristics and learning pattern of individual students, teachers can provide individualized learning strategies based on students' needs. Students' learning patterns may experience changes depending on their conditions during the learning process. If the learning pattern analysis is only run once, then the progress and changes in student learning patterns throughout the learning process cannot be recognized. On the other hand, periodical analysis is expected to describe the dynamics of student learning patterns from time to time. This research is intended for capturing students' dynamic learning pattern using Hierarchical Clustering. We clustered the learning patterns based on Learning Management Systems (LMS) activity logs. The activity log data were partitioned into several periodical datasets. The results of the periodic clustering indicated that students’ learning patterns varied from one another and changed from time to time. Most students experienced change in learning patterns throughout the semester. The analysis also indicated that learning pattern also has the potential to be improved and maintained.Students can have various characteristics and learning patterns. By understanding the characteristics and learning pattern of individual students, teachers can provide individualized learning strategies based on students' needs. Students' learning patterns may experience changes depending on their conditions during the learning process. If the learning pattern analysis is only run once, then the progress and changes in student learning patterns throughout the learning process cannot be recognized. On the other hand, periodical analysis is expected to describe the dynamics of student learning patterns from time to time. This research is intended for capturing students' dynamic learning pattern using Hierarchical Clustering. We clustered the learning patterns based on Learning Management Systems (LMS) activity logs. The activity log data were partitioned into several periodical datasets. The results of the periodic clustering indicated that students’ learning patterns varied from one another and changed from time to time. Most students experienced change in learning patterns throughout the semester. The analysis also indicated that learning pattern also has the potential to be improved and maintained
Sunflower Image Classification Using Multiclass Support Vector Machine Based on Histogram Characteristics
Get PDFSunflower is an important commodity in agriculture, besides being used as an ornamental plant, sunflower is an oil-producing plant and a source of industrial materials. In Indonesia, sunflower productivity is considered less than optimal, because knowledge and information about sunflowers are still lacking. Therefore, information is needed that can be used as an extension of knowledge about sunflowers itself, especially in Indonesia, which is a tropical region which is an area suitable for the growth of sunflowers. Sunflowers can actually be identified based on recognizable traits. However, the similar shape makes it difficult for some people to distinguish the types of sunflowers. This study aims to classify sunflower images using a first-order feature extraction algorithm using the characteristics of mean, skewness, variance, kurtosis, and entropy which are then used as input to the Multiclass SVM identification algorithm. Data points are mapped to dimensionless space using a Multiclass SVM to produce hyperplane-linear separation between each class. Based on the results of testing the accuracy of the model is able to perform classification with an average accuracy of 79%. These results show that the developed model can classify well.
Sunflower is an important commodity in agriculture, besides being used as an ornamental plant, sunflower is an oil-producing plant and a source of industrial materials. In Indonesia, sunflower productivity is considered less than optimal, because knowledge and information about sunflowers are still lacking. Therefore, information is needed that can be used as an extension of knowledge about sunflowers itself, especially in Indonesia, which is a tropical region which is an area suitable for the growth of sunflowers. Sunflowers can actually be identified based on recognizable traits. However, the similar shape makes it difficult for some people to distinguish the types of sunflowers. This study aims to classify sunflower images using a first-order feature extraction algorithm using the characteristics of mean, skewness, variance, kurtosis, and entropy which are then used as input to the Multiclass SVM identification algorithm. Data points are mapped to dimensionless space using a Multiclass SVM to produce hyperplane-linear separation between each class. Based on the results of testing the accuracy of the model is able to perform classification with an average accuracy of 79%. These results show that the developed model can classify well
Design Factors in Evaluating and Formulating IT Governance Systems in Public Organizations
No full textThe application of information technology (IT) by central and regional governments to public services is intended to efficiently and effectively improve performance and public services. However, many studies have shown the ineffective application of IT. In Gorontalo province, this can be seen in the e-readiness value of Gorontalo province as a prerequisite for successful IT implementation, which is still at 58.15 points, which means that it is at a moderate level of readiness. This shows that implementing IT governance in the local government of Gorontalo province is still not optimal in terms of performance or public services. This study aims to identify the design factors that need to be considered when implementing IT Governance to achieve better public service performance. This study uses a quantitative approach based on a survey method. The results showed six models at Level 3: BAI06, BAI07, DSS01, DSS03, DSS04, and DSS05. In addition, four models were at level 4: APO12, APO13, BAI10, and DSS02. Levels 3 and 4 show that the IT governance capability of the Gorontalo provincial government in each model is not yet optimal. This study recommends that the Gorontalo provincial government evaluate and formulate an effective IT Governance system by focusing on each model's IT Governance design factors to improve public service performance.
 
Scrum Maturity Level Evaluation and Improvement Recommendation: Case Study on ABC Application
Get PDFBank XYZ, one of the digital banking in Indonesia, has a digital product ABC for customers to complete online banking transactions. Bank XYZ uses Scrum as the methodology to develop ABC. Several problems were found in the process related to the delay in the release process. The achievement of sprint goals from May to December 2021 is only 6%. This fact allegedly caused some frequent release delays. To resolve the root causes, mixed-method research was conducted to provide recommendations for improving the implementation of Scrum. The Scrum Maturity Model questionnaires were distributed to several Scrum teams, followed by interviews with several roles that were used to validate the results. The key process area rating formula of the Agile maturity model was used to decide the maturity level. After the maturity level result was obtained, recommendation practices were generated from the not well-implemented practice. This case-based research shows that Bank XYZ reached maturity level 2 for ABC development. Bank XYZ has implemented 78 out of 79 practices, however, 28 practices need improvement and one practice needs to be applied. Objectives of maturity levels group recommendation practices. The combination of Scrum best practices and empirical practices from previous research generated those practices. This research was intended to give general recommendations on how to improve Scrum implementation and on how to resolve release time problems by enhancing Scrum in Bank XYZ empirically
Klasifikasi Serangan Jaringan untuk Investigasi Forensik Jaringan: Tinjauan Literatur
No full textThe computer network plays an important role in supporting various jobs and other activities in the cyber world. Various kinds of crimes have often occurred on computer networks. It is very demanding to build a computer network architecture that is safe from attacks to protect the data transacted. If there has been an attack on the computer network, of course, further investigation must be carried out to identify the attacker and the motive for the attack. An additional need is to evaluate the security of the network. This article reports a systematic review of the literature aiming to map the classification of attacks on computer networks and map future research. Based on the exploration, 30 key studies were selected that reveal the mapping of attack classifications on computer networks. The results of the literature review show that attacks on computer networks vary widely. Based on the results of the literature review conducted, it produces a roadmap for future research, which is to classify attacks on computer networks using a machine learning approach. The use of machine learning serves to help classify and investigate the needs for attacks on computer networks. The SVM method in this case was chosen based on previous research that was widely used for data-based classification.Jaringan Komputer memegang peranan penting untuk mendukung berbagai pekerjaan dan aktivitas lainnya di dunia cyber. Berbagai macam kejahatan yang dilakukan pada jaringan komputer sudah sering terjadi. Hal ini sangat menuntut untuk membangun sebuah arsitektur jaringan komputer yang aman dari serangan untuk melindungi data-data yang ditransaksikan. Jika telah terjadi serangan pada jaringan komputer, tentunya harus dilakukan investigasi lebih lanjut untuk kebutuhan identifikasi penyerang dan motif dari serangan tersebut. Kebutuhan lebih lanjut adalah mengevaluasi keamanan jaringan. Makalah ini melaporkan tinjauan literatur sistematis yang bertujuan untuk memetakan klasifikasi serangan terhadap jaringan komputer dan memetakan penelitian di masa depan. Berdasarkan hasil eksplorasi, dipilih 30 penelitian utama yang mengungkapkan pemetaan klasifikasi serangan pada jaringan komputer. Hasil tinjauan literatur menunjukkan bahwa serangan pada jaringan komputer sangat bervariasi. Berdasarkan hasil tinjauan pustaka yang dilakukan, menghasilkan peta jalan untuk penelitian ke depan, yaitu melakukan klasifikasi serangan pada jaringan komputer dengan menggunakan pendekatan machine learning. Penggunaan machine learning berfungsi untuk membantu mengklasifikasikan dan menginvestigasi kebutuhan serangan pada jaringan komputer. Metode SVM dalam hal ini dipilih berdasarkan penelitian sebelumnya yang banyak digunakan untuk klasifikasi berbasis data
Implementasi Algoritma K-Means untuk Clustering Project Health pada PT XYZ
No full textIndonesia has several companies that are involved in the telecommunications sector. Various projects run in parallel to support the success of telecommunications companies. The potential of a project can increase company revenue and productivity. On the other hand, there are some risks that need to be considered for every project when it is about to start. Project data is recorded from start to finish so that the project's progress and improvements can be monitored and analyzed. As the project runs, the project team at one of Indonesia's telecommunication companies, which is responsible for the processes leading to project success, requires a project health category. Therefore, this study is conducted to develop a clustering project health process, which is included in a type of unsupervised learning that runs on unlabeled data. One of the clustering algorithms is K-Means, which groups data based on similar criteria. Researchers also use dimensionality reduction with the principal component analysis (PCA) method to determine its impact on the clustering process with the K-Means algorithm. From this study, the researcher obtained three groups or project health categories, consisting of groups 0, 1, and 2. The evaluation results with the Calinski-Harabasz index showed that the K-Means model in the PCA dimensionality reduction data performed better than the standard K-Means model with a Calinski-Harabasz index value of 55633,12776405707, which is higher than 25914,578262576793.PT XYZ merupakan salah satu perusahaan di Indonesia yang bergerak di bidang telekomunikasi. Dalam menunjang kesuksesannya, terdapat berbagai proyek yang berjalan secara paralel. Seiring berjalannya proyek, tim project assurance pada PT XYZ yang bertanggung jawab terhadap proses menuju kesuksesan proyek membutuhkan suatu kategori project health. Oleh karena itu, peneliti melakukan proses clustering project health yang termasuk ke dalam jenis unsupervised learning. Salah satu algoritma clustering adalah K-Means yang mengelompokkan data berdasarkan kriteria-kriteria yang serupa. Peneliti juga menggunakan reduksi dimensi dengan metode PCA untuk mengetahui pengaruhnya terhadap proses clustering dengan K-Means. Peneliti melakukan penelitian dengan evaluasi cluster menggunakan Calinski-Harabasz Index untuk mengetahui bagaimana perbedaan antar-cluster dan kemiripan anggota dalam cluster yang sama. Dari penelitian ini, diperoleh hasil tiga kategori project health yang terdiri dari cluster 0, 1, dan 2. Hasil evaluasi dengan Calinski-Harabasz Index menunjukkan bahwa model K-Means pada data hasil reduksi dimensi dengan PCA memiliki performa yang lebih baik dibandingkan model K-Means standar dengan nilai Calinski-Harabasz Index lebih tinggi yaitu sebesar 55633,12776405707 dibandingkan dengan 25914,578262576793
Date Fruit Classification using K-Nearest Neighbor with Principal Component Analysis and Binary Particle Swarm Optimization
Get PDFVarious cultivars of date fruits distributed throughout exhibit diverse complexity and unique attributes, including color, flavor, shape, and texture. These distinctive characteristics and appearance occasionally lack variability in date fruits, since various kinds of date fruit may have subtle differences in color, shape, and texture. To overcome the difficulty of sorting and classifying multiple types of date fruit, a classification model was developed to categorize date fruit according to their visual appearances and digital characteristics. This study proposes a classification system that categorizes date fruit into five distinct types. The system achieves this by extracting features related to date fruit images' color, shape, and texture. Specifically, color moments, HOG descriptors, and circularity are used for feature extraction. The resulting high-quality training data is then used to train a K-Nearest-Neighbor (KNN) classifier. Considering the parameters applied to develop the proposed classification model is essential. Therefore, the proposed KNN model will be optimized by Principal Component Analysis (PCA) and Binary Particle Swarm Optimization (BPSO). PCA is employed for dimensionality reduction, whereas BPSO is implemented to discover the optimal neighbors. The experimental results demonstrated that the classification model achieved an accuracy of 93.85%, a considerable improvement of 12% over barebone KNN
ANoM STEMMER: Nazief & Andriani Modification for Madurese Stemming
Get PDFMadurese is one of the regional languages in Indonesia. This is a cultural property that needs to be preserved. With various uniqueness and word formation rules, the Madurese language can be used in information retrieval, namely stemming. The Madurese language has a close relationship with the Javanese language; in several studies, the stemming method is often used, such as the modification of the Nazief and Adriani method, which has good performance for the Javanese language, but there has never been any research on the Madurese language and it has not been proven successful. Previous studies also have not used morphophonemic rules that influence word formation in Madurese. Therefore, this research was developed by modifying Nazief and Adriani's algorithm for Madurese based on Madurese language morphology by removing affixes, namely ter-ater (prefix), panoteng (suffix), and morphophonemic rules. Corpus uses 1000 words from the Madurese language dictionary that have received affixes. The accuracy of the algorithm is 89% with 890 words that match; the prefix has an accuracy of 93.81%; the suffix has an accuracy of 83.78%; and the confix has an accuracy of 80.07%. As for the overall performance, it produces an accuracy of 89.0% with an error rate of 11%. Understemming is found in 104 words, and overstemming in 6 words. The time it takes to compile is 31.31 seconds.
Bahasa Madura adalah salah satu bahasa daerah di Indonesia yang digunakan oleh orang Madura di pulau Madura sendiri atau yang tinggal di luar pulau seperti Jawa, Kalimantan, bahkan di luar negeri. Bahasa Madura terkait dengan bahasa Jawa. Kaidah pembentukan kata dalam bahasa Madura dapat digunakan dalam Information Retrieval yaitu stemming. Beberapa metode stemming sering digunakan seperti modifikasi metode Nazief & Adriani yang memiliki performa yang baik untuk bahasa Jawa, namun untuk penelitian bahasa Madura belum pernah ada dan belum terbukti berhasil. Penelitian sebelumnya juga belum menggunakan kaidah morfofonemik yang mempengaruhi pembentukan kata dalam bahasa Madura. Oleh karena itu penelitian ini dikembangkan dengan memodifikasi algoritma Nazief & Adriani untuk bahasa Madura berdasarkan morfologi bahasa Madura dengan menghilangkan afiks yaitu ter-ater (awalan), panoteng (akhiran), dan kaidah morfofonemik. Corpus menggunakan 1000 kata dari kamus bahasa Madura yang sudah mendapat imbuhan. Keakuratan algoritma adalah 89%, dengan 890 kata yang cocok, 104 kata pada understemming dan 6 kata overs pada temming .